Aerosol Oxidative Potential in the Greater Los Angeles Area: Source Apportionment and Associations with Socioeconomic Position

Oxidative potential (OP) has been proposed as a possible integrated metric for particles smaller than 2.5 μm in diameter (PM2.5) to evaluate adverse health outcomes associated with particulate air pollution exposure. Here, we investigate how OP depends on sources and chemical composition and how OP varies by land use type and neighborhood socioeconomic position in the Los Angeles area. We measured OH formation (OPOH), dithiothreitol loss (OPDTT), black carbon, and 52 metals and elements for 54 total PM2.5 samples collected in September 2019 and February 2020. The Positive Matrix Factorization source apportionment model identified four sources contributing to volume-normalized OPOH: vehicular exhaust, brake and tire wear, soil and road dust, and mixed secondary and marine. Exhaust emissions contributed 42% of OPOH, followed by 21% from brake and tire wear. Similar results were observed for the OPDTT source apportionment. Furthermore, by linking measured PM2.5 and OP with census tract level socioeconomic and health outcome data provided by CalEnviroScreen, we found that the most disadvantaged neighborhoods were exposed to both the most toxic particles and the highest particle concentrations. OPOH exhibited the largest inverse social gradients, followed by OPDTT and PM2.5 mass. Finally, OPOH was the metric most strongly correlated with adverse health outcome indicators.


Figures
. Sample site locations and types during two seasons in September 2019 and February 2020 in Greater Los Angeles. Figure S2. Relationships between OPm OH and OPm DTT with PM2.5 mass and each other. Figure S3. Correlation heatmap showing Spearman's r values for mass-normalized OP and selected elements. Figure S4. PMF predicted vs. measured OPv OH for 54 samples. Figure S5. Factor profiles of the OPv DTT PMF model. Figure S6. (a) The average contribution of PMF-resolved sources to the OPv DTT for all sites (both seasons included) with standard error of the mean and (b) for each site category. Figure S7. PMF predicted vs. measured OPv DTT for 54 samples. Figure S8. Contribution of four emission sources to the OPv OH for different socioeconomic position groups. Figure S9. BC, Cu, Fe and Mn relative to their average concentration for each quartile of socioeconomic classification. Tables   Table S1. Spearman's correlations for PM2.5 mass, OP and socioeconomic factors. Table S2. Spearman's correlations between PM2.5 mass or OP and exposure indicators from CalEnviroScreen for the 51 census tracts sampled.

S1.1 Sampling Site Map
Figure S1. Sample site locations and types during two seasons in September 2019 and February 2020 in Greater Los Angeles. See also Oroumiyeh et al. 1 for a detailed description of the site classifications.

S1.2 Black carbon (BC) and 52 Elements Quantification
We estimated BC on filters from measurements of optical absorption at 370 and 880 nm using an Optical Transmissometer (Magee Scientific). Teflon filters were placed on quartz filters to obtain an even light distribution on the detector. The instrument reports incident and transmitted light, I 0

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and I, respectively. For a filter with sampled volume of air, V and filter collection area S, we can calculate the absorption coefficient based on Beer's law using the following equation: There are two dominant artifacts associated with filter-based absorption measurements. One is the scattering by the filter fibers, leading to increased light attenuation from multiple opportunities for absorptions, and the other is the shadowing by the particles deposited upon one another, as particles are not perfectly loaded in a single layer. This causes an underestimation of the true attenuation.
We corrected the multi-scattering issue and loading effect for the absorption coefficient using the

S1.4 The Hydroxyl Radical (OH) Assay
The Fluorescence Spectrometer (Thermo Scientific). The yield of TAOH is pH dependent and is 33% at pH 7.3. 8 A calibration curve for TAOH ranging from 0 -800 nM was constructed daily. The measured OH formation rate for blank filters was about 3 ± 0.5 nM/min. The average blank corrected OH formation rate for samples was about 14 nM/min, an order of magnitude larger than three times of the standard deviation of blanks.

S1.5 The Dithiothreitol (DTT) Assay
For the DTT assay, we based our procedure on Cho et al. 9 . The DTT solution used in the assay

S1.6 PMF Source Apportionment Analysis
PMF is based on the chemical mass balance equation: Where GH refers to a speciated data set with i samples and j number of species; p refers to the number of factors; GI is the contribution of the k th factor to i th sample; IH is the loading of j th species in the k th factor; and GH represents the residual error for the i th sample and j th species.
Where n and m refer to the number of samples and species; and GH is the measurement uncertainty associated with the i th sample and j th species, respectively. 13,14 In this study, the OP OH , OP DTT and BC experimental uncertainties were assumed to be three times the standard deviation of the blank measurements, corresponding to a confidence interval of 99.7%.
Subsequently, the estimated experimental uncertainty was converted into uncertainty for the volume-normalized OP OH , OP DTT and BC concentrations using the general laws of uncertainty. 15 Uncertainties for the elements were derived by propagating the three major sources of analytical uncertainty: (i) SF-ICPMS measurement; (ii) method blank; and (iii) digestion uncertainty.
Lastly, base model error estimation methods were applied to evaluate the rotational ambiguity and random errors of selected PMF runs. The base model displacement (DISP) analysis requires the decrease in PMF-resolved Q to be < 1% along with no factor swaps for the smallest dQmax = 4.

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Additionally, to be considered valid, PMF runs were required to have at least 80% of the factors mapped in the Bootstrap (BS) analysis. 13,16 Section S2. Relationships between PM2.5 Mass Concentration and Mass-normalized OP OH and OP DTT Figure S2. Relationships between OPm OH and OPm DTT with PM2.5 mass and each other. p < 0.05.

Section S3. Correlations between OP and BC, Elements
To understand how OP depends on different chemical components, we calculated Spearman's correlations (r s ) for both volume-normalized and mass-normalized OP OH and OP DTT with BC and 52 elements. For the volume-normalized data, significant correlations were observed between OP and most of the elements, many of which likely resulted from strong correlations between the element and PM 2.5 mass concentration, making the data difficult to interpret. Mass-normalized OP OH and OP DTT correlations with measured elements were not as strong. Figure S3 shows

Section S4. Source Apportionment Results
Uncertainty associated with our OP v OH and OP v DTT was evaluated using the Displacement (DISP) and Bootstrap (BS) error estimation tools in PMF. The DISP explores the rotational ambiguity in a PMF solution by assessing the largest range of source profile values without an appreciable increase in the Q-value. 13 For both OP v OH and OP v DTT PMF models, the decrease in Q was small S11 (dQ < 0.01% and < 1% for OP v OH and OP v DTT , respectively) and there was no factor swap present for the smallest dQ max (dQ max = 4), suggesting there was negligible rotational ambiguity on OP v OH PMF solution and small rotational ambiguity on OP v DTT solution. In the BS error estimate, we performed 100 BS runs.
There was no unmapped factor for either the OP v OH or OP v DTT PMF solutions. In the OP v OH PMF solution, 100% of the BS profile was mapped to the base profile for brake and tire wear, mixed secondary and marine and soil and road dust and 81% was mapped for vehicular exhaust source.
In the OP v DTT PMF model, the mapping of BS factors to base profiles ranged from 87% of vehicular exhaust and road dust to 100% of mixed secondary and marine aerosols. Mapping over 80% of all factors indicates random errors were relatively small in our PMF models. In addition, we obtained relatively high R 2 values between the predicted and measured target variables (0.92 and 0.78 for OP v OH and OP v DTT , respectively) as shown in Figure S4 and S7. Altogether, this indicates our PMF models are relatively stable and have acceptable statistical characteristics. Figure S4. PMF predicted vs. measured OPv OH for 54 samples.